Papers with cross entropy loss
Domain-specific transformer models for query translation (2023.acl-industry)
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| Challenge: | In domains such as Grocery, users prefer to buy certain brands of products . a large non-English speaking population makes it difficult to translate code-mix queries . |
| Approach: | They propose a model to preserve/correct Grocery brand names while translating context words . they propose to use a dataset of popular Groceries brand names to train the model . |
| Outcome: | The proposed model preserves/corrects Grocery brand names while translating context words . it is tested with a large non-English speaking population and is deployed in production . |
Encouraging Neural Machine Translation to Satisfy Terminology Constraints (2021.findings-acl)
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| Challenge: | a new approach to encourage neural machine translation to satisfy lexical constraints is proposed . a BLEU score and percentage of generated constraint terms are improved by the proposed method . |
| Approach: | They propose a method that encourages neural machine translation to satisfy lexical constraints at training step . they use a simplified augmentation strategy without source factors and constraint token masking to make it easier to learn the copy behavior . |
| Outcome: | The proposed method improves on baselines in terms of BLEU score and percentage of generated constraint terms. |
Generative Spoken Language Model based on continuous word-sized audio tokens (2023.emnlp-main)
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| Challenge: | Text-based language models outperform character-based models, but speech inputs are 20ms or 40ms-long discrete units. |
| Approach: | They propose a generative language model based on word-size continuous audio tokens . they replace lookup table for lexical types with a Lexical Embedding function . |
| Outcome: | The proposed model is five times more memory efficient than discrete unit GSLMs and is phonetically and semantically interpretable. |
Document-Level Event Argument Extraction by Leveraging Redundant Information and Closed Boundary Loss (2022.naacl-main)
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| Challenge: | Document-level event argument extraction is a crucial subtask of event extraction. |
| Approach: | They propose to use redundant event information to extract multiple arguments from a document . they propose a loss function to classify Universum class by their open decision boundary . |
| Outcome: | The proposed model outperforms the previous state-of-the-art models by 3.35% in F1-score. |
Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation (2021.findings-emnlp)
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| Challenge: | Radiology report generation aims at generating descriptive text from radiology images automatically. |
| Approach: | They propose a weakly supervised contrastive loss method that generates descriptive text from radiology images automatically. |
| Outcome: | The proposed method outperforms previous work on correctness and text generation metrics for two public benchmarks. |